Upload app.py

app.py

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+import torch
+import re
+import gradio as gr
+from pathlib import Path
+from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel
+
+def predict(image, max_length=30, num_beams=4):
+    image = image.convert('RGB')
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+    with torch.no_grad():
+        caption_ids = model.generate(pixel_values.cpu())[0]
+        caption_text = tokenizer.decode(caption_ids, skip_special_tokens=True)
+    return caption_text
+    
+model_path = "MahsaShahidi/Persian-Image-Captioning"
+device = "cpu"
+# Load model.
+model = VisionEncoderDecoderModel.from_pretrained(model_path)
+model.to(device)
+print("Loaded model")
+feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+print("Loaded feature_extractor")
+tokenizer = AutoTokenizer.from_pretrained('HooshvareLab/bert-fa-base-uncased-clf-persiannews')
+print("Loaded tokenizer")
+title = "Persian Image Captioning"
+description = ""
+
+input = gr.inputs.Image(label="Image to search", type = 'pil', optional=False)
+output = gr.outputs.Textbox(type="auto",label="Captions")
+
+article = "This HuggingFace Space presents a demo for Persian Image Camptioning on VIT as its Encoder and ParsBERT (v2.0) as its Decoder"
+
+images = [f"./image-{i}.jpg" for i in range(1,4)]
+
+interface = gr.Interface(
+        fn=predict,
+        inputs = input,
+        outputs=output,
+        examples = images,
+        title=title,
+        description=article,
+    )
+interface.launch(share = True)